.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "build/examples_action_recognition/dive_deep_tsn_ucf101.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_build_examples_action_recognition_dive_deep_tsn_ucf101.py: 2. Dive Deep into Training TSN mdoels on UCF101 ================================================== This is a video action recognition tutorial using Gluon CV toolkit, a step-by-step example. The readers should have basic knowledge of deep learning and should be familiar with Gluon API. New users may first go through `A 60-minute Gluon Crash Course `_. You can `Start Training Now`_ or `Dive into Deep`_. Start Training Now ~~~~~~~~~~~~~~~~~~ .. note:: Feel free to skip the tutorial because the training script is self-complete and ready to launch. :download:`Download Full Python Script: train_recognizer.py<../../../scripts/action-recognition/train_recognizer.py>` Example training command:: # Finetune a pretrained VGG16 model without using temporal segment network. python train_recognizer.py --model vgg16_ucf101 --num-classes 101 --num-gpus 8 --lr-mode step --lr 0.001 --lr-decay 0.1 --lr-decay-epoch 30,60,80 --num-epochs 80 # Finetune a pretrained VGG16 model using temporal segment network. python train_recognizer.py --model vgg16_ucf101 --num-classes 101 --num-gpus 8 --num-segments 3 --lr-mode step --lr 0.001 --lr-decay 0.1 --lr-decay-epoch 30,60,80 --num-epochs 80 For more training command options, please run ``python train_recognizer.py -h`` Please checkout the `model_zoo <../model_zoo/index.html#action_recognition>`_ for training commands of reproducing the pretrained model. Network Structure ----------------- First, let's import the necessary libraries into python. .. GENERATED FROM PYTHON SOURCE LINES 36-54 .. code-block:: default from __future__ import division import argparse, time, logging, os, sys, math import numpy as np import mxnet as mx import gluoncv as gcv from mxnet import gluon, nd, init, context from mxnet import autograd as ag from mxnet.gluon import nn from mxnet.gluon.data.vision import transforms from gluoncv.data.transforms import video from gluoncv.data import UCF101 from gluoncv.model_zoo import get_model from gluoncv.utils import makedirs, LRSequential, LRScheduler, split_and_load, TrainingHistory .. GENERATED FROM PYTHON SOURCE LINES 55-66 Video action recognition is a classification problem. Here we pick a simple yet well-performing structure, ``vgg16_ucf101``, for the tutorial. In addition, we use the the idea of temporal segments (TSN) [Wang16]_ to wrap the backbone VGG16 network for adaptation to video domain. `TSN `_ is a widely adopted video classification method. It is proposed to incorporate temporal information from an entire video. The idea is straightforward: we can evenly divide the video into several segments, process each segment individually, obtain segmental consensus from each segment, and perform final prediction. TSN is more like a general algorithm, rather than a specific network architecture. It can work with both 2D and 3D neural networks. .. GENERATED FROM PYTHON SOURCE LINES 67-77 .. code-block:: default # number of GPUs to use num_gpus = 1 ctx = [mx.gpu(i) for i in range(num_gpus)] # Get the model vgg16_ucf101 with temporal segment network, with 101 output classes, without pre-trained weights net = get_model(name='vgg16_ucf101', nclass=101, num_segments=3) net.collect_params().reset_ctx(ctx) print(net) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ActionRecVGG16( (features): HybridSequential( (0): Conv2D(3 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Activation(relu) (2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): Activation(relu) (4): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW) (5): Conv2D(64 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): Activation(relu) (7): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): Activation(relu) (9): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW) (10): Conv2D(128 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): Activation(relu) (12): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): Activation(relu) (14): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): Activation(relu) (16): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW) (17): Conv2D(256 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): Activation(relu) (19): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): Activation(relu) (21): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): Activation(relu) (23): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW) (24): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): Activation(relu) (26): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): Activation(relu) (28): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): Activation(relu) (30): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW) (31): Dense(25088 -> 4096, Activation(relu)) (32): Dropout(p = 0.9, axes=()) (33): Dense(4096 -> 4096, Activation(relu)) (34): Dropout(p = 0.9, axes=()) ) (output): Dense(4096 -> 101, linear) ) .. GENERATED FROM PYTHON SOURCE LINES 78-87 Data Augmentation and Data Loader --------------------------------- Data augmentation for video is different from image. For example, if you want to randomly crop a video sequence, you need to make sure all the video frames in this sequence undergo the same cropping process. We provide a new set of transformation functions, working with multiple images. Please checkout the `video.py <../../../gluoncv/data/transforms/video.py>`_ for more details. Most video data augmentation strategies used here are introduced in [Wang15]_. .. GENERATED FROM PYTHON SOURCE LINES 87-101 .. code-block:: default transform_train = transforms.Compose([ # Fix the input video frames size as 256×340 and randomly sample the cropping width and height from # {256,224,192,168}. After that, resize the cropped regions to 224 × 224. video.VideoMultiScaleCrop(size=(224, 224), scale_ratios=[1.0, 0.875, 0.75, 0.66]), # Randomly flip the video frames horizontally video.VideoRandomHorizontalFlip(), # Transpose the video frames from height*width*num_channels to num_channels*height*width # and map values from [0, 255] to [0,1] video.VideoToTensor(), # Normalize the video frames with mean and standard deviation calculated across all images video.VideoNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) .. GENERATED FROM PYTHON SOURCE LINES 102-104 With the transform functions, we can define data loaders for our training datasets. .. GENERATED FROM PYTHON SOURCE LINES 104-120 .. code-block:: default # Batch Size for Each GPU per_device_batch_size = 5 # Number of data loader workers num_workers = 0 # Calculate effective total batch size batch_size = per_device_batch_size * num_gpus # Set train=True for training the model. Here we set num_segments to 3 to enable TSN training. # Make sure you have UCF101 data prepared # Please checkout the `ucf101.py <../../../../datasets/ucf101.py>`_ for more details. train_dataset = UCF101(train=True, num_segments=3, transform=transform_train) print('Load %d training samples.' % len(train_dataset)) train_data = gluon.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Load 9537 training samples. .. GENERATED FROM PYTHON SOURCE LINES 121-123 Optimizer, Loss and Metric -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 123-137 .. code-block:: default # Learning rate decay factor lr_decay = 0.1 # Epochs where learning rate decays lr_decay_epoch = [30, 60, np.inf] # Stochastic gradient descent optimizer = 'sgd' # Set parameters optimizer_params = {'learning_rate': 0.001, 'wd': 0.0001, 'momentum': 0.9} # Define our trainer for net trainer = gluon.Trainer(net.collect_params(), optimizer, optimizer_params) .. GENERATED FROM PYTHON SOURCE LINES 138-141 In order to optimize our model, we need a loss function. For classification tasks, we usually use softmax cross entropy as the loss function. .. GENERATED FROM PYTHON SOURCE LINES 141-144 .. code-block:: default loss_fn = gluon.loss.SoftmaxCrossEntropyLoss() .. GENERATED FROM PYTHON SOURCE LINES 145-148 For simplicity, we use accuracy as the metric to monitor our training process. Besides, we record metric values, and will print them at the end of training. .. GENERATED FROM PYTHON SOURCE LINES 148-152 .. code-block:: default train_metric = mx.metric.Accuracy() train_history = TrainingHistory(['training-acc']) .. GENERATED FROM PYTHON SOURCE LINES 153-162 Training -------- After all the preparations, we can finally start training! Following is the script. .. note:: In order to finish the tutorial quickly, we only train for 3 epochs, and 100 iterations per epoch. In your experiments, we recommend setting ``epochs=80`` for the full UCF101 dataset. .. GENERATED FROM PYTHON SOURCE LINES 162-215 .. code-block:: default epochs = 3 lr_decay_count = 0 for epoch in range(epochs): tic = time.time() train_metric.reset() train_loss = 0 # Learning rate decay if epoch == lr_decay_epoch[lr_decay_count]: trainer.set_learning_rate(trainer.learning_rate*lr_decay) lr_decay_count += 1 # Loop through each batch of training data for i, batch in enumerate(train_data): # Extract data and label data = split_and_load(batch[0], ctx_list=ctx, batch_axis=0) label = split_and_load(batch[1], ctx_list=ctx, batch_axis=0) # AutoGrad with ag.record(): output = [] for _, X in enumerate(data): X = X.reshape((-1,) + X.shape[2:]) pred = net(X) output.append(pred) loss = [loss_fn(yhat, y) for yhat, y in zip(output, label)] # Backpropagation for l in loss: l.backward() # Optimize trainer.step(batch_size) # Update metrics train_loss += sum([l.mean().asscalar() for l in loss]) train_metric.update(label, output) if i == 100: break name, acc = train_metric.get() # Update history and print metrics train_history.update([acc]) print('[Epoch %d] train=%f loss=%f time: %f' % (epoch, acc, train_loss / (i+1), time.time()-tic)) # We can plot the metric scores with: train_history.plot() .. image-sg:: /build/examples_action_recognition/images/sphx_glr_dive_deep_tsn_ucf101_001.png :alt: dive deep tsn ucf101 :srcset: /build/examples_action_recognition/images/sphx_glr_dive_deep_tsn_ucf101_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [Epoch 0] train=0.037624 loss=4.593155 time: 43.816577 [Epoch 1] train=0.077228 loss=4.329765 time: 42.983821 [Epoch 2] train=0.095050 loss=4.159773 time: 43.229185 .. GENERATED FROM PYTHON SOURCE LINES 216-231 You can `Start Training Now`_. If you would like to use a bigger 3D model (e.g., I3D) on a larger dataset (e.g., Kinetics400), feel free to read the next `tutorial on Kinetics400 `__. References ---------- .. [Wang15] Limin Wang, Yuanjun Xiong, Zhe Wang, and Yu Qiao. \ "Towards Good Practices for Very Deep Two-Stream ConvNets." \ arXiv preprint arXiv:1507.02159 (2015). .. [Wang16] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang and Luc Van Gool. \ "Temporal Segment Networks: Towards Good Practices for Deep Action Recognition." \ In European Conference on Computer Vision (ECCV). 2016. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 2 minutes 11.163 seconds) .. _sphx_glr_download_build_examples_action_recognition_dive_deep_tsn_ucf101.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: dive_deep_tsn_ucf101.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: dive_deep_tsn_ucf101.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_